The third benefit is its capability of fully exploiting the increasing amount of sensor data and computational power.
Deep learning or deep neural network has the capacity to scale to large-scale problems. The huge amount of parameters inside a DNN framework are automatically optimized by minimizing a loss function, by training on large datasets through backpropagation and gradient-descent algorithms.
For example, the recent released GPT-3[1]}, the largest pretrained language model, with incredibly over 175 Billion parameters,
achieves the state-of-the-art results on a variety of natural language processing (NLP) tasks, even without fine-tuning.
In addition, a variety of large-scale datasets relevant to localization and mapping have been released, for example, in the autonomous vehicles scenarios, [2]}, [3]}, [4]} are with a collection of rich combinations of sensor data, and motion and semantic labels.
This gives us an imagination that it would be possible to exploit the power of data and computation in solving localization and mapping.
